PagedAttention — 3 Technical Articles

Apr 27, 2026 · Artificial Intelligence

vLLM Production Pitfalls: The Ultimate Fix for PagedAttention Memory Fragmentation and OOM

This article analyzes why vLLM's PagedAttention can cause GPU memory fragmentation and out‑of‑memory errors in production, presents four typical OOM scenarios, and provides concrete diagnostics, configuration tweaks, code examples, and monitoring strategies to eliminate the problem.

CUDAGPU memoryLLM serving

0 likes · 22 min read

vLLM Production Pitfalls: The Ultimate Fix for PagedAttention Memory Fragmentation and OOM

Ops Community

Jan 18, 2026 · Artificial Intelligence

How to Quadruple LLM Throughput with vLLM’s PagedAttention and Continuous Batching

This guide details how to replace native Transformers inference with the high‑performance vLLM engine, leveraging PagedAttention, continuous batching, tensor parallelism, and OpenAI‑compatible APIs to achieve 3‑4× higher throughput, lower latency, and scalable multi‑GPU deployments for production‑grade large language models.

Continuous batchingGPU OptimizationOpenAI API Compatibility

0 likes · 61 min read

How to Quadruple LLM Throughput with vLLM’s PagedAttention and Continuous Batching

Baobao Algorithm Notes

Apr 5, 2024 · Artificial Intelligence

How vLLM’s PagedAttention Revolutionizes GPU Memory Management for LLM Inference

This article explains how vLLM’s PagedAttention, inspired by operating‑system virtual‑memory paging, dynamically allocates KV‑cache memory to dramatically reduce GPU memory fragmentation, improve throughput, and handle scheduling, preemption, and distributed inference for large language models.

GPU memoryLLM inferencePagedAttention

0 likes · 25 min read

How vLLM’s PagedAttention Revolutionizes GPU Memory Management for LLM Inference